There are a number of applications in today's industrial and manufacturing environments for machines that operate at slow speed. The efficient operation and maintenance of these machines is essential to maximizing production and minimizing downtime. Latent or incipient failures of these machines often go undetected due to the difficulty in visually detecting faults such as a divot or flat spot in a slow speed bearing.
There are known techniques for monitoring medium and high speed machinery by analyzing mechanical vibrations generated by the machinery during operation. An accelerometer is typically used to generate a signal corresponding to the vibrations produced by the machine as it operates. The signal produced by the accelerometer will include normal (nonfault) vibration components as well as fault related vibration components occurring within the machine. Various analytic techniques are then used to separate the vibration signal into its constituent components and identify those components corresponding to possible faults and other mechanical vibrations.
Unfortunately, the techniques which are used to monitor for faults within medium and high speed machinery are not equally effective for monitoring faults within slow speed machinery. There is a special difficulty in monitoring low rpm vibration produced by low speed machines because the low frequency components will typically be of lesser amplitude than the higher frequency components. The higher amplitude, higher frequency components of the vibration signal will tend to drown out the low amplitude, low frequency vibrations of interest, producing an unacceptably high signal-to-noise ratio. Further complicating the processing of vibration signals produced by slow speed machines is that the sensor (accelerometer) adds an electrical noise component to the composite signal. The problem for the monitoring system becomes one of extracting the low level components (requires a wide dynamic range) from the composite signal and then differentiating the vibration components of interest from the other low level components generated from electrical noise sources, temperature transients, and other similar sources of irrelevant components. Thus, the analysis of the vibration signal must become more sensitive to the low level (and often low frequency) components when monitoring slow speed machinery. Existing analysis techniques are unable to effectively adjust to these higher sensitivity requirements and process the vibration signal without eliminating the low level components.
The sensitivity required of the sensor is based on the amount of displacement to be sensed. For high sensitivity applications, the sensor of choice for vibration analysis is typically an accelerometer having a dynamic range of between 100 to 120 dB. It is desirable that the analysis method be able to accommodate this range. Most analysis methods, however, result in an attenuation of the accelerometer signal so that the dynamic range of the analyzed signal is, for example, reduced to about 80 dB. Thus, there is a need for a measurement system having a dynamic response that is at least as good as that of the accelerometer.
Although there are no universally-accepted criteria for classifying machinery as slow speed, intermediate speed, or high speed, it is generally recognized that slow speed machines are those that operate at or below 600 rpm. The value of 600 rpm is significant because it represents the approximate speed at which alert levels relative to vibration in the velocity domain must be reduced as the speed of the machine decreases. A commonly accepted methodology is to establish alert levels which decrease linearly with decreasing speed (or constant in the displacement domain).
Thus, there is a need for a reliable and effective measurement system for monitoring slow speed machinery that is capable of differentiating fault-induced vibration components from machinery-induced vibration components as well as other extraneous components, including electrical noise and transients. The system should be minimally responsive to temperature transients and maintain a wide dynamic range.